I have been working on the HEVC project. I have recently asked some details with regards to Intra Prediction and it is more or less clarified. I was reading a book suggested by someone and it gives the details of the Transformation algorithm implemented in HEVC. I know that it uses Partial Butterfly in order to process data. However, would it be possible to implement via a different approach like say matrix multiplication and still the HEVC stream would be generated with no faults. What my question is that, whether if I modify the processing method for Transformation Module will it affect the process flow of HEVC Encoder on the whole.
Yes, it is possible. I have implemented the Matrix Multiplication in place of Partial Butterfly for my solution. It works fine, the output looks good. I have confirmed that the PSNR SSIM and other parameters are good enough to confirm this for you.
The transform has to comply with the HEVC standard, but how you implement it exactly is up to you.
It just implementation issue, Matrix Multiplication can optimization by using other method like SIMD.
Related
Just a general question here, no reproducible example but thought this might be the right place anyway since its very software specific.
I am building a model which I want to convert to .tflite. It relies on tf.hub.text_embedding_collumn() for feature generation. When I convert to .tflite will this be captured such that the resulting model will take raw text as input rather than a sparse vector representation?
Would be good to know just generally before I invest too much time in this approach. Thanks in advance!
Currently I don't imagine this would work, as we do not support enough string ops to implement that. One approach would be to do this handling through a custom op, but implementing this custom op would require domain knowledge and mitigate the ease-of-use advance of using tf hub in the first place.
There is some interest in defining a set of hub operators that are verified to work well with tflite, but this is not yet ready.
Has anyone seen any work done on this? I'd think this would be a reasonably common use-case. Train model in python, export the graph and map to a sequence of eigen instructions?
I don't believe anything like this is available, but it is definitely something that would be useful. There are some obstacles to overcome though:
Not all operations are implemented by Eigen.
We'd need to know how to generate code for all operations we want to support.
The glue code to allocate buffers and schedule work can get pretty gnarly.
It's still a good idea though, and it might get more attention posted as a feature request on https://github.com/tensorflow/tensorflow/issues/
I parsed the HEVC stream by simply identifying sart code (000001 or 00000001), and now I am looking for the motion information in the NAL payload. My goal is to calculate the percentage of the motion information in the stream. Any ideas?
Your best bet is to start with the HM reference software (get it here: https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware/trunk/) and add some debug info as the different kinds of data is read from the bitstream. This is likely much easier than writing bitstream decoder from scratch.
Check out the debug that is built into the software already, for example RExt__DECODER_DEBUG_BIT_STATISTICS or DEBUG_CABAC_BINS. This may do what you want already, if not it will be pretty close. I think information about bit usage can be best collected in source/Lib/TLibDecoder/TDecBinCoderCABAC.cpp during decode.
If you need to speed this up, you can of course skip the actual decode steps :)
At the decoder side, You can find the motion vector information as MVD, so you should using pixel decoding process to get the motion information. it need you to understand the process of the inter prediction at HEVC.
than you!
I want to use feature selection to find the terms in a document that are most useful for a binary classification task.
I've been looking around:
This mentions Mutual Information and the chi-squared test metric
http://nlp.stanford.edu/IR-book/html/htmledition/feature-selection-1.html
MATLAB has a number of functions as well:
http://www.mathworks.com/help/toolbox/stats/brj0qbu.html
Feature Selection in MATLAB
Of the above, relieff and rankfeatures look promising.
I do not know if my data follows a normal distribution. Any thoughts on which technique performs the best? Are there any newer methods you would suggest? The focus is to increase classification accuracy.
Thank you!
Since the answer is highly dependent on the nature of your data, I'd suggest playing with several options, possibly using a hold-out set for verification.
The easiest path would probably be to use Weka or RapidMiner for experimenting. Choosing from the plethora of options provided by them, you'll probably get acquainted with several other methods.
Having said that, I have found Mutual Information/Infogain to be useful on a large variety of problems.
Is anyone aware of a reasonably well documented example of simulated annealing in Visual Basic that I can examine and adapt?
This project looks pretty well documented: http://www.codeproject.com/KB/recipes/simulatedAnnealingTSP.aspx. It's C# but contains only one important source file (TravellingSalesmanProblem.cs) so it's pretty easy to run it through a converter. Maybe: http://labs.developerfusion.co.uk/convert/csharp-to-vb.aspx?
MSDN magazine also had an interesting article on neural networks. As I understand simulated annealing, you can add it to other function estimation methods (like neural nets). So you could add simulated annealing to the MSDN VB code by shrinking the Momentum over time. The network starts 'hot' by backpropagating error with a large Momentum and slowly 'cools' by shrinking the Momentum and thus reducing the effect of output error in backpropagation.
Cheers.
I generally refer to "Numerical recipes in C/C++" for all the pseudocode and adapt to my own later. That is the best documentation/implementation you could find. Sometimes you could even find better algorithms or an alternative way of solving. (In case Newton Raphshon is not the way to go)